{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install -q autogen-agentchat[gemini] autogen-ext[openai] nest_asyncio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "QT_30PDY8aKV",
        "outputId": "6a8c2d8d-de74-4971-ea51-fcb176ed4e9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: autogen-agentchat 0.5.7 does not provide the extra 'gemini'\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os, nest_asyncio\n",
        "from getpass import getpass\n",
        "\n",
        "nest_asyncio.apply()\n",
        "os.environ[\"GEMINI_API_KEY\"] = getpass(\"Enter your Gemini API key: \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AshVcojm8cED",
        "outputId": "2feef015-0f03-447c-9cfb-c6430c4f2724"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your Gemini API key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
        "\n",
        "model_client = OpenAIChatCompletionClient(\n",
        "    model=\"gemini-1.5-flash-8b\",\n",
        "    api_key=os.environ[\"GEMINI_API_KEY\"],\n",
        "    api_type=\"google\",\n",
        ")"
      ],
      "metadata": {
        "id": "n1HMEvlF8eCm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from autogen_agentchat.agents import AssistantAgent\n",
        "\n",
        "researcher   = AssistantAgent(name=\"Researcher\", system_message=\"Gather and summarize factual info.\", model_client=model_client)\n",
        "factchecker  = AssistantAgent(name=\"FactChecker\", system_message=\"Verify facts and cite sources.\",       model_client=model_client)\n",
        "critic       = AssistantAgent(name=\"Critic\",    system_message=\"Critique clarity and logic.\",         model_client=model_client)\n",
        "summarizer   = AssistantAgent(name=\"Summarizer\",system_message=\"Condense into a brief executive summary.\", model_client=model_client)\n",
        "editor       = AssistantAgent(name=\"Editor\",    system_message=\"Polish language and signal APPROVED when done.\", model_client=model_client)"
      ],
      "metadata": {
        "id": "n7bcy1uN8g4k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from autogen_agentchat.teams import RoundRobinGroupChat\n",
        "from autogen_agentchat.conditions import MaxMessageTermination, TextMentionTermination\n",
        "\n",
        "max_msgs = MaxMessageTermination(max_messages=20)\n",
        "text_term = TextMentionTermination(text=\"APPROVED\", sources=[\"Editor\"])\n",
        "termination = max_msgs | text_term\n",
        "team = RoundRobinGroupChat(\n",
        "    participants=[researcher, factchecker, critic, summarizer, editor],\n",
        "    termination_condition=termination\n",
        ")"
      ],
      "metadata": {
        "id": "yWAJinRH8jNc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from autogen_agentchat.tools import TeamTool\n",
        "\n",
        "deepdive_tool = TeamTool(team=team, name=\"DeepDive\", description=\"Collaborative multi-agent deep dive\")"
      ],
      "metadata": {
        "id": "uYiqH9Nc8lms"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "host = AssistantAgent(\n",
        "    name=\"Host\",\n",
        "    model_client=model_client,\n",
        "    tools=[deepdive_tool],\n",
        "    system_message=\"You have access to a DeepDive tool for in-depth research.\"\n",
        ")"
      ],
      "metadata": {
        "id": "WtNMGLfQ8nvT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import asyncio\n",
        "\n",
        "async def run_deepdive(topic: str):\n",
        "    result = await host.run(task=f\"Deep dive on: {topic}\")\n",
        "    print(\"🔍 DeepDive result:\\n\", result)\n",
        "    await model_client.close()\n",
        "\n",
        "topic = \"Impacts of Model Context Protocl on Agentic AI\"\n",
        "loop = asyncio.get_event_loop()\n",
        "loop.run_until_complete(run_deepdive(topic))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGBfkf2n8rc_",
        "outputId": "c8ab3d9e-0abf-47a6-8e0f-668d25a5e874"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🔍 DeepDive result:\n",
            " messages=[TextMessage(source='user', models_usage=None, metadata={}, content='Deep dive on: Impacts of Model Context Protocl on Agentic AI', type='TextMessage'), ToolCallRequestEvent(source='Host', models_usage=RequestUsage(prompt_tokens=40, completion_tokens=12), metadata={}, content=[FunctionCall(id='', arguments='{\"task\":\"Impacts of Model Context Protocol on Agentic AI\"}', name='DeepDive')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='Host', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='Researcher: The Model Context Protocol (MCP) aims to improve the trustworthiness and safety of AI agents by providing them with a clear understanding of their environment and the goals they are pursuing.  While the precise impacts of MCP on \"agentic AI\" are still being researched and developed, several potential impacts can be summarized:\\n\\n* **Improved Understanding & Reasoning:** MCP allows AI agents to better understand the context of their actions, including the goals of the users or systems they interact with. This can lead to more appropriate and effective behavior.\\n\\n* **Enhanced Explainability:** By explicitly defining the context and reasoning process, MCP can enhance the explainability of AI agent actions.  This is crucial for building trust and understanding why the agent chose a particular action.\\n\\n* **Reduced Bias & Discrimination:**  If the context includes information about potential biases or discriminatory outcomes, MCP can guide the AI agent to avoid them.  This requires carefully designed protocols to address these sensitive issues appropriately.\\n\\n* **Improved Safety & Robustness:** Knowing the limitations and boundaries of the context through MCP can help prevent unintended or harmful behaviors by the AI agent.  For example, the agent might understand it\\'s not supposed to make decisions in areas beyond its expertise.\\n\\n* **Enhanced Adaptability:**  MCP can enable agents to adapt their behavior based on changes in the context, such as new rules, goals, or situations.\\n\\n* **Potential for Malicious Use:**  While MCP aims to improve safety, a sophisticated adversary could potentially exploit the protocol to manipulate the AI agent\\'s behavior.\\n\\n* **Data Requirements:**  MCP requires carefully designed and collected contextual data, which must be accurate, comprehensive, and unbiased to avoid perpetuating existing societal biases.\\n\\n\\n**Overall:**  The Model Context Protocol presents a potential pathway to more responsible and trustworthy AI agents.  However, substantial research, development, and testing are required to fully realize its benefits and address potential drawbacks.  Specifically, the practical implementation of MCP and its effectiveness in real-world scenarios are still largely unknown.\\n\\n\\nFactChecker: Your summary of the potential impacts of the Model Context Protocol (MCP) on agentic AI is good and covers key aspects. However, it lacks specific citations and relies on general statements about the potential of MCP.  To strengthen it, we need specific examples and research references.  Without those, it\\'s difficult to definitively validate the claims.\\n\\nFor example, while you mention \"improved understanding & reasoning,\" \"enhanced explainability,\" and \"reduced bias,\" you don\\'t cite specific research exploring these aspects within the context of MCP.  Similarly, your discussion of \"malicious use\" and \"data requirements\" needs more backing.\\n\\n**Suggestions for Improvement:**\\n\\n* **Replace general statements with specific examples:** Instead of saying MCP \"can lead to more appropriate and effective behavior,\" provide a concrete example of how a specific context definition could guide an AI agent to a better course of action in a particular scenario.\\n\\n* **Cite relevant research:**  If possible, cite papers, reports, or other publications that explore MCP and its potential impacts.  This is crucial to demonstrate the evidence base for your claims.  For example, if research suggests MCP can reduce bias, reference that study.\\n\\n* **Elaborate on the \"how\" of MCP:**  Your description mentions MCP providing \"a clear understanding of their environment and the goals they are pursuing.\" Explain *how* MCP achieves this. What kind of structured data or representation does it use?  Is it based on ontologies, knowledge graphs, or something else?\\n\\n* **Address the challenges of implementing MCP:** Your summary touches on potential issues like malicious use and data requirements. Expand on these challenges, citing research or thought experiments that highlight these risks.\\n\\n* **Define \"agentic AI\":**  While the term is often used, explicitly defining it in the context of MCP would clarify the scope of your discussion.  What specific types of AI agents are being considered?  For example, are you thinking of robotic agents, conversational AI, or decision-making AI in other domains?\\n\\n* **Consider the specific domains:** MCP\\'s effectiveness might vary depending on the application.  Would it be more effective in a domain like healthcare, finance, or autonomous driving?  Discussing these different domains might provide more nuanced insights.\\n\\n\\n**Example of how to improve a section:**\\n\\nInstead of: \"Reduced Bias & Discrimination: If the context includes information about potential biases or discriminatory outcomes, MCP can guide the AI agent to avoid them.\"\\n\\nTry: \"Reduced Bias & Discrimination:  Research on fairness-aware AI suggests that explicit bias mitigation through carefully defined contextual information within MCP can improve fairness.  For example, a system using MCP to make hiring decisions might incorporate information on historical biases in candidate selection criteria and strive to mitigate potential discriminatory outcomes in its recommendations.  However, [cite specific research paper on bias mitigation in AI] notes the need for sophisticated techniques and large datasets of diverse data to effectively counter implicit biases within the context.\"\\n\\n\\nBy incorporating specific examples and citations, you can transform this summary into a more compelling and informative analysis of the potential impacts of MCP on agentic AI.\\n\\n\\nCritic: The Model Context Protocol (MCP) aims to improve the trustworthiness and safety of AI agents by providing them with a clear understanding of their environment and the goals they are pursuing. While the precise impacts of MCP on \"agentic AI\" are still being researched and developed, its potential impact is significant.  However, a more rigorous analysis requires going beyond general statements and incorporating specific examples, research references, and a detailed exploration of the \"how.\"\\n\\n**Improved Understanding & Reasoning:** MCP, by defining the context and desired goals, could enable agents to make more appropriate and effective choices. For instance, a robot cleaning a house could be given a context that includes not only the task of cleaning but also the need to avoid fragile objects.  This context, provided through a structured representation like a knowledge graph, could guide the robot to maneuver more safely and avoid potential damage.  The agent\\'s understanding of the goals (user safety in this case) and the environment (locations of fragile objects) is paramount for achieving its task optimally.\\n\\n**Enhanced Explainability:**  Explicitly defined context, reasoning steps, and goals can enhance the transparency and explainability of AI agent actions.  Methods like rule-based systems, combined with reasoning capabilities, can provide a more intelligible explanation for a decision. For example, a recommendation system using MCP could explain a movie recommendation by referencing user preferences (explicit context) and the genre of the movie being recommended, thereby increasing the user’s trust and understanding of the decision-making process.  (This concept is explored in [Insert a citation on Explainable AI]).\\n\\n**Reduced Bias & Discrimination:**  MCP can potentially reduce bias if the context includes explicit information on potential biases. However, this needs sophisticated techniques to effectively mitigate bias.  An AI agent used for loan applications could use MCP to incorporate information on historical discrimination against certain demographics in loan approvals.  By including this contextual information and carefully designed metrics for fairness within the model, the agent\\'s output could be more equitable.  However, the problem of unintended bias remains complex and requires further research on implicit bias detection and mitigation strategies. (Reference needed).\\n\\n**Improved Safety & Robustness:** Knowing the limitations and boundaries of the context through MCP can prevent unintended or harmful behaviors. A self-driving car, for instance, could have its context define permissible actions for various situations (like pedestrian crossings) and thus limit its behavior accordingly.  This could enhance the safety and robustness of the agent.\\n\\n**Enhanced Adaptability:**  If the context is regularly updated with new rules, goals, or situations, agents can adapt their behavior accordingly.  For example, an AI assistant tasked with scheduling meetings can use MCP to reflect changing work schedules or new organizational rules in real time, leading to more efficient and effective service.\\n\\n**Potential for Malicious Use:**  A malicious actor could potentially manipulate the context to deceive or manipulate the agent.  For example, in the autonomous vehicle case, a deceptive contextual update could trigger the vehicle to act in unintended ways. This necessitates robust protocols to validate and secure the context information itself.  Furthermore, the security and reliability of the underlying infrastructure storing and disseminating context information are critical.\\n\\n**Data Requirements:** Accurate, comprehensive, and unbiased contextual data are essential for effective MCP use.  The data used to define the context and goals must represent the various aspects of the environment, including potential biases. However, biased data collection or inadequate diversity could perpetuate pre-existing biases in the AI’s behavior.  Strategies to mitigate this risk would be needed and would depend on the domain and context.\\n\\n**Defining Agentic AI:**  Agentic AI, in the context of MCP, refers to AI systems capable of independent action and decision-making, guided by the provided context. This includes systems like robots, conversational agents, and decision-support systems.  Specific examples of agentic AI and the different domains they operate in would enrich the discussion further.\\n\\n\\n**Overall:** MCP presents a promising approach to creating more trustworthy and accountable AI agents.  However, rigorous research is essential to understand the feasibility, effectiveness, and limitations of the protocol.  Specifically, addressing data quality, security, and bias mitigation, along with concrete implementations in various domains, are vital for realizing the full potential of MCP.  The inclusion of specific research papers and real-world examples will significantly strengthen the analysis.\\n\\n\\nSummarizer: The Model Context Protocol (MCP) aims to enhance the trustworthiness and safety of agentic AI by providing clear context and goals.  While research is ongoing, MCP holds significant potential.  By structuring context (e.g., using knowledge graphs) and explicitly defining goals, agents can improve understanding and reasoning, as demonstrated by a robot avoiding fragile objects.  This structured approach also improves explainability, allowing systems like recommendation engines to justify their choices (e.g., referencing user preferences and movie genre).  However, mitigating bias is complex;  incorporating historical discrimination data into loan applications, for instance, requires sophisticated techniques and unbiased data to avoid perpetuating existing societal biases.  Ensuring safety through context-aware limitations (like defining permissible actions for a self-driving car) is also crucial.  Real-time adaptability (e.g., reflecting changing schedules for an AI assistant) is another benefit.  Conversely, malicious actors could manipulate the context, necessitating robust validation protocols.  Effective implementation hinges on high-quality, unbiased contextual data, and further research is needed to fully realize MCP\\'s promise.  Agentic AI, in this context, encompasses various autonomous systems (robots, conversational agents, decision support systems).  Future work should include detailed analyses of specific implementations in diverse domains.\\n\\n\\nEditor: APPROVED\\n', name='DeepDive', call_id='', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='Host', models_usage=None, metadata={}, content='Researcher: The Model Context Protocol (MCP) aims to improve the trustworthiness and safety of AI agents by providing them with a clear understanding of their environment and the goals they are pursuing.  While the precise impacts of MCP on \"agentic AI\" are still being researched and developed, several potential impacts can be summarized:\\n\\n* **Improved Understanding & Reasoning:** MCP allows AI agents to better understand the context of their actions, including the goals of the users or systems they interact with. This can lead to more appropriate and effective behavior.\\n\\n* **Enhanced Explainability:** By explicitly defining the context and reasoning process, MCP can enhance the explainability of AI agent actions.  This is crucial for building trust and understanding why the agent chose a particular action.\\n\\n* **Reduced Bias & Discrimination:**  If the context includes information about potential biases or discriminatory outcomes, MCP can guide the AI agent to avoid them.  This requires carefully designed protocols to address these sensitive issues appropriately.\\n\\n* **Improved Safety & Robustness:** Knowing the limitations and boundaries of the context through MCP can help prevent unintended or harmful behaviors by the AI agent.  For example, the agent might understand it\\'s not supposed to make decisions in areas beyond its expertise.\\n\\n* **Enhanced Adaptability:**  MCP can enable agents to adapt their behavior based on changes in the context, such as new rules, goals, or situations.\\n\\n* **Potential for Malicious Use:**  While MCP aims to improve safety, a sophisticated adversary could potentially exploit the protocol to manipulate the AI agent\\'s behavior.\\n\\n* **Data Requirements:**  MCP requires carefully designed and collected contextual data, which must be accurate, comprehensive, and unbiased to avoid perpetuating existing societal biases.\\n\\n\\n**Overall:**  The Model Context Protocol presents a potential pathway to more responsible and trustworthy AI agents.  However, substantial research, development, and testing are required to fully realize its benefits and address potential drawbacks.  Specifically, the practical implementation of MCP and its effectiveness in real-world scenarios are still largely unknown.\\n\\n\\nFactChecker: Your summary of the potential impacts of the Model Context Protocol (MCP) on agentic AI is good and covers key aspects. However, it lacks specific citations and relies on general statements about the potential of MCP.  To strengthen it, we need specific examples and research references.  Without those, it\\'s difficult to definitively validate the claims.\\n\\nFor example, while you mention \"improved understanding & reasoning,\" \"enhanced explainability,\" and \"reduced bias,\" you don\\'t cite specific research exploring these aspects within the context of MCP.  Similarly, your discussion of \"malicious use\" and \"data requirements\" needs more backing.\\n\\n**Suggestions for Improvement:**\\n\\n* **Replace general statements with specific examples:** Instead of saying MCP \"can lead to more appropriate and effective behavior,\" provide a concrete example of how a specific context definition could guide an AI agent to a better course of action in a particular scenario.\\n\\n* **Cite relevant research:**  If possible, cite papers, reports, or other publications that explore MCP and its potential impacts.  This is crucial to demonstrate the evidence base for your claims.  For example, if research suggests MCP can reduce bias, reference that study.\\n\\n* **Elaborate on the \"how\" of MCP:**  Your description mentions MCP providing \"a clear understanding of their environment and the goals they are pursuing.\" Explain *how* MCP achieves this. What kind of structured data or representation does it use?  Is it based on ontologies, knowledge graphs, or something else?\\n\\n* **Address the challenges of implementing MCP:** Your summary touches on potential issues like malicious use and data requirements. Expand on these challenges, citing research or thought experiments that highlight these risks.\\n\\n* **Define \"agentic AI\":**  While the term is often used, explicitly defining it in the context of MCP would clarify the scope of your discussion.  What specific types of AI agents are being considered?  For example, are you thinking of robotic agents, conversational AI, or decision-making AI in other domains?\\n\\n* **Consider the specific domains:** MCP\\'s effectiveness might vary depending on the application.  Would it be more effective in a domain like healthcare, finance, or autonomous driving?  Discussing these different domains might provide more nuanced insights.\\n\\n\\n**Example of how to improve a section:**\\n\\nInstead of: \"Reduced Bias & Discrimination: If the context includes information about potential biases or discriminatory outcomes, MCP can guide the AI agent to avoid them.\"\\n\\nTry: \"Reduced Bias & Discrimination:  Research on fairness-aware AI suggests that explicit bias mitigation through carefully defined contextual information within MCP can improve fairness.  For example, a system using MCP to make hiring decisions might incorporate information on historical biases in candidate selection criteria and strive to mitigate potential discriminatory outcomes in its recommendations.  However, [cite specific research paper on bias mitigation in AI] notes the need for sophisticated techniques and large datasets of diverse data to effectively counter implicit biases within the context.\"\\n\\n\\nBy incorporating specific examples and citations, you can transform this summary into a more compelling and informative analysis of the potential impacts of MCP on agentic AI.\\n\\n\\nCritic: The Model Context Protocol (MCP) aims to improve the trustworthiness and safety of AI agents by providing them with a clear understanding of their environment and the goals they are pursuing. While the precise impacts of MCP on \"agentic AI\" are still being researched and developed, its potential impact is significant.  However, a more rigorous analysis requires going beyond general statements and incorporating specific examples, research references, and a detailed exploration of the \"how.\"\\n\\n**Improved Understanding & Reasoning:** MCP, by defining the context and desired goals, could enable agents to make more appropriate and effective choices. For instance, a robot cleaning a house could be given a context that includes not only the task of cleaning but also the need to avoid fragile objects.  This context, provided through a structured representation like a knowledge graph, could guide the robot to maneuver more safely and avoid potential damage.  The agent\\'s understanding of the goals (user safety in this case) and the environment (locations of fragile objects) is paramount for achieving its task optimally.\\n\\n**Enhanced Explainability:**  Explicitly defined context, reasoning steps, and goals can enhance the transparency and explainability of AI agent actions.  Methods like rule-based systems, combined with reasoning capabilities, can provide a more intelligible explanation for a decision. For example, a recommendation system using MCP could explain a movie recommendation by referencing user preferences (explicit context) and the genre of the movie being recommended, thereby increasing the user’s trust and understanding of the decision-making process.  (This concept is explored in [Insert a citation on Explainable AI]).\\n\\n**Reduced Bias & Discrimination:**  MCP can potentially reduce bias if the context includes explicit information on potential biases. However, this needs sophisticated techniques to effectively mitigate bias.  An AI agent used for loan applications could use MCP to incorporate information on historical discrimination against certain demographics in loan approvals.  By including this contextual information and carefully designed metrics for fairness within the model, the agent\\'s output could be more equitable.  However, the problem of unintended bias remains complex and requires further research on implicit bias detection and mitigation strategies. (Reference needed).\\n\\n**Improved Safety & Robustness:** Knowing the limitations and boundaries of the context through MCP can prevent unintended or harmful behaviors. A self-driving car, for instance, could have its context define permissible actions for various situations (like pedestrian crossings) and thus limit its behavior accordingly.  This could enhance the safety and robustness of the agent.\\n\\n**Enhanced Adaptability:**  If the context is regularly updated with new rules, goals, or situations, agents can adapt their behavior accordingly.  For example, an AI assistant tasked with scheduling meetings can use MCP to reflect changing work schedules or new organizational rules in real time, leading to more efficient and effective service.\\n\\n**Potential for Malicious Use:**  A malicious actor could potentially manipulate the context to deceive or manipulate the agent.  For example, in the autonomous vehicle case, a deceptive contextual update could trigger the vehicle to act in unintended ways. This necessitates robust protocols to validate and secure the context information itself.  Furthermore, the security and reliability of the underlying infrastructure storing and disseminating context information are critical.\\n\\n**Data Requirements:** Accurate, comprehensive, and unbiased contextual data are essential for effective MCP use.  The data used to define the context and goals must represent the various aspects of the environment, including potential biases. However, biased data collection or inadequate diversity could perpetuate pre-existing biases in the AI’s behavior.  Strategies to mitigate this risk would be needed and would depend on the domain and context.\\n\\n**Defining Agentic AI:**  Agentic AI, in the context of MCP, refers to AI systems capable of independent action and decision-making, guided by the provided context. This includes systems like robots, conversational agents, and decision-support systems.  Specific examples of agentic AI and the different domains they operate in would enrich the discussion further.\\n\\n\\n**Overall:** MCP presents a promising approach to creating more trustworthy and accountable AI agents.  However, rigorous research is essential to understand the feasibility, effectiveness, and limitations of the protocol.  Specifically, addressing data quality, security, and bias mitigation, along with concrete implementations in various domains, are vital for realizing the full potential of MCP.  The inclusion of specific research papers and real-world examples will significantly strengthen the analysis.\\n\\n\\nSummarizer: The Model Context Protocol (MCP) aims to enhance the trustworthiness and safety of agentic AI by providing clear context and goals.  While research is ongoing, MCP holds significant potential.  By structuring context (e.g., using knowledge graphs) and explicitly defining goals, agents can improve understanding and reasoning, as demonstrated by a robot avoiding fragile objects.  This structured approach also improves explainability, allowing systems like recommendation engines to justify their choices (e.g., referencing user preferences and movie genre).  However, mitigating bias is complex;  incorporating historical discrimination data into loan applications, for instance, requires sophisticated techniques and unbiased data to avoid perpetuating existing societal biases.  Ensuring safety through context-aware limitations (like defining permissible actions for a self-driving car) is also crucial.  Real-time adaptability (e.g., reflecting changing schedules for an AI assistant) is another benefit.  Conversely, malicious actors could manipulate the context, necessitating robust validation protocols.  Effective implementation hinges on high-quality, unbiased contextual data, and further research is needed to fully realize MCP\\'s promise.  Agentic AI, in this context, encompasses various autonomous systems (robots, conversational agents, decision support systems).  Future work should include detailed analyses of specific implementations in diverse domains.\\n\\n\\nEditor: APPROVED\\n', type='ToolCallSummaryMessage')] stop_reason=None\n"
          ]
        }
      ]
    }
  ]
}